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Image domain multi-material decomposition using single energy CT.

Yi Xue1,2, Chen Luo1,2, Yangkang Jiang1

  • 1Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, People's Republic of China.

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This study introduces a new multi-material decomposition (MMD) method using single-energy CT (SECT) for material composition quantification. The edge-preserving (EP) method significantly improves accuracy and reduces noise compared to existing techniques.

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Imaging

Background:

  • Multi-material decomposition (MMD) is crucial for material composition quantification in medical imaging.
  • Current MMD methods often rely on spectral or dual-energy CT (DECT), requiring hardware modifications.
  • Single-energy CT (SECT) offers a more accessible alternative but faces challenges in MMD implementation.

Purpose of the Study:

  • To develop and evaluate an image-domain MMD method using SECT data.
  • To improve material composition quantification accuracy and image quality in CT scans.
  • To provide a hardware-independent MMD solution for clinical practice.

Main Methods:

  • Proposed an objective function with a least-squares data fidelity term and an edge-preserving (EP) regularization term.
  • Utilized the optimization transfer principle to create a pixel-wise separable quadratic surrogate (PWSQS) function for efficient minimization.
  • Employed a pixelwise direct inversion method with the two-material assumption (TMA) for initial value estimation.

Main Results:

  • The TMA method achieved a volume fraction accuracy (VFA) of 79.64% in phantom studies.
  • The EP method further improved VFA by 15.56% and reduced decomposition standard deviation (STD) by 81.51% compared to TMA.
  • In clinical data, the generated virtual non-contrast image had a root-mean-squared-relative error of 2.93% against the ground truth.

Conclusions:

  • The proposed image-domain MMD method using SECT is effective for material composition quantification.
  • The edge-preserving regularization significantly enhances accuracy and reduces noise in decomposed material images.
  • This SECT-based MMD approach offers a promising, hardware-independent alternative for clinical applications.